The overuse of antibiotics in food animals has led to the development of bacterial resistance and the widespread of resistant bacteria in the world. Antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs) in food animals are currently considered emerging contaminants, which are a serious threat to public health globally. The current situation of ARB and ARGs from food animal farms, manure, and the wastewater was firstly covered in this review. Potential risks to public health were also highlighted, as well as strategies (including novel technologies, alternatives, and administration) to fight against bacterial resistance. This review can provide an avenue for further research, development, and application of novel antibacterial agents to reduce the adverse effects of antibiotic resistance in food animal farms.

Red wine is a well-known alcoholic beverage, and is known to have phenolic compounds (PCs), which contribute to its antioxidant activity and have other beneficial advantages for human health. The aim of this study was to evaluate the effect of the simulated gastro-intestinal digestion and the Caco-2 transepithelial transport assay on the PCs, bioavailability, and the antioxidant capacity of red wines. The contents of PCs in red wine were significantly reduced during most of the digestion phases. Phenolic acid had the greatest permeability, while the flavonols had the weakest. The bioavailability of PCs ranged from 2.08 to 24.01%. The result of the partial least squares structural equation model showed that the three phenols were positively correlated with the antioxidant activity of red wine. The contribution of anthocyanins was the largest (0.8667).

Escherichia coli is one of the most frequent causes of gastro-intestinal and extra-intestinal diseases in animals and humans. Due to overuse and misuse of antibiotics, recent years have seen a rapidly increasing prevalence of antibiotic-resistant (AR) Escherichia coli globally; particularly, AR E. coli from farm animal-associated sources and its antibiotic resistance genes (ARGs) are becoming a global concern, with clinical negative effects on both human and animal health. The aim of this review was to explore the prevalence trends of AR E. coli from farm animals, waste treatment, and aquatic environments. The disinfection methods of AR E. coli and possible alternatives to antibiotics were also highlighted. The current review highlights that the prevalence of AR E. coli from food animals, products, and animal waste is increasing at an alarming rate, but is reduced at waste treatment plants. Ultraviolet (UV) treatment, surface plasma oxidation, and biochar are commonly used to effectively eliminate AR E. coli. Some probiotics, plant extracts, and antimicrobial peptides (AMPs) are arousing interest as promising alternatives to antibiotics to fight against AR E. coli. The current review suggests that AR E. coli from farm animal-associated sources is prevalent and poses a serious global threat to public health. This review provides an avenue for further research, development, and application of novel strategies to minimize antibiotic resistance in E. coli of farm animal origin.

Effective prediction of wastewater treatment is beneficial for precise control of wastewater treatment processes. The nonlinearity of pollutant indicators such as chemical oxygen demand (COD) and total phosphorus (TP) makes the model difficult to fit and has low prediction accuracy. The classical deep learning methods have been shown to perform nonlinear modeling. However, there are enormous numerical differences between multi-dimensional data in the prediction problem of wastewater treatment, such as COD above 3000 mg/L and TP around 30 mg/L. It will make current normalization methods challenging to handle effectively, leading to the training failing to converge and the gradient disappearing or exploding. This paper proposes a multi-factor prediction model based on deep learning. The model consists of a combined normalization layer and a codec. The combined normalization layer combines the advantages of three normalization calculation methods: z-score, Interval, and Max, which can realize the adaptive processing of multi-factor data, fully retain the characteristics of the data, and finally cooperate with the codec to learn the data characteristics and output the prediction results. Experiments show that the proposed model can overcome data differences and complex nonlinearity in predicting industrial wastewater pollutant indicators and achieve better prediction accuracy than classical models.